Section: Scientific Foundations
Structure of nonlinear control systems
In most problems, choosing the adapted coordinates, or the right quantities that describe a phenomenon, sheds light on a path to the solution. In control systems, it is often crucial to analyze the structure of the model, deduced from physical principles, of the plant of be controlled; this may lead to putting it via some transformations in a simpler form, or a form that is most suitable for control design. For instance, equivalence to a linear system may allow to use linear control; also, the so-called “flatness” property drastically simplifies path planning [48] , [63] .
A better understanding of the “set of nonlinear models”, partly classifying them, has another motivation than facilitating control design for a given system and its model: it may also be a necessary step towards a theory of “nonlinear identification” and modeling. Linear identification is a mature area of control science; its success is mostly due to a very fine knowledge of the structure of the class of linear models: similarly, any progress in the understanding of the structure of the class of nonlinear models would be a contribution to a possible theory of nonlinear identification.
These topics are central in control theory, but raise very difficult mathematical questions: static feedback
classification is a geometric problem feasible in principle, although describing invariants explicitly is
technically very difficult; and conditions for dynamic feedback equivalence and linearization raise unsolved
mathematical problems, that make one wonder about decidability (Consider the simple system with state